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  2. Statistical significance - Wikipedia

    en.wikipedia.org/wiki/Statistical_significance

    More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; [4] and the p-value of a result, , is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. [5]

  3. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    The solution to this question would be to report the p-value or significance level α of the statistic. For example, if the p-value of a test statistic result is estimated at 0.0596, then there is a probability of 5.96% that we falsely reject H 0. Or, if we say, the statistic is performed at level α, like 0.05, then we allow to falsely reject ...

  4. Size (statistics) - Wikipedia

    en.wikipedia.org/wiki/Size_(statistics)

    It is denoted by the Greek letter α (alpha). For a simple hypothesis, = (). In the case of a composite null hypothesis, the size is the supremum over all data generating processes that satisfy the null hypotheses. [1]

  5. p-value - Wikipedia

    en.wikipedia.org/wiki/P-value

    In a significance test, the null hypothesis is rejected if the p-value is less than or equal to a predefined threshold value , which is referred to as the alpha level or significance level. α {\displaystyle \alpha } is not derived from the data, but rather is set by the researcher before examining the data.

  6. Misuse of p-values - Wikipedia

    en.wikipedia.org/wiki/Misuse_of_p-values

    The 0.05 significance level is merely a convention. [3] [5] The 0.05 significance level (alpha level) is often used as the boundary between a statistically significant and a statistically non-significant p-value. However, this does not imply that there is generally a scientific reason to consider results on opposite sides of any threshold as ...

  7. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    Suppose the data can be realized from an N(0,1) distribution. For example, with a chosen significance level α = 0.05, from the Z-table, a one-tailed critical value of approximately 1.645 can be obtained. The one-tailed critical value C α ≈ 1.645 corresponds to the chosen significance level.

  8. Bonferroni correction - Wikipedia

    en.wikipedia.org/wiki/Bonferroni_correction

    The Bonferroni correction can also be applied as a p-value adjustment: Using that approach, instead of adjusting the alpha level, each p-value is multiplied by the number of tests (with adjusted p-values that exceed 1 then being reduced to 1), and the alpha level is left unchanged. The significance decisions using this approach will be the same ...

  9. Analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_variance

    If one's F-statistic is greater in magnitude than their critical value, we can say there is statistical significance at the 0.05 alpha level. The F-test is used for comparing the factors of the total deviation. For example, in one-way, or single-factor ANOVA, statistical significance is tested for by comparing the F test statistic